A common surveillance problem is encountered when tracking a potentially large number of targets based on measurements originating from a potentially large number of sensors which may be separated over long distances. The sensors provide information regarding parameters for the location and the change in location of the targets to a central location relative to the sensor's position. Such parameters may include bearing, range, bearing rate, velocity, or position. Typically, the measurements are corrupted by random noise and deterministic bias, both of which may vary.
This problem of multitarget, multisensor detection and tracking has been approached from a number of perspectives, such as described in Multitarget, Multisensor tracking: Advanced Applications, by Y.Bar-Shalom, ed., published in 1990 by Artech House, Inc. One approach, as described in "An Algorithm for Tracking Multiple Targets," by D. B. Reid, published in IEEE Transactions on Automatic Control, Vol. AC24, No. 6, December 1979, is to use multi-hypothesis tracking. In multi-hypothesis tracking, an association is first assumed between the measurements collected and the potential targets and for every assumption, i.e. hypothesis, the target positions are updated using the measurements in question as input signals to a filter, such as a Kalman filter. The resulting error between the measurement and the filtered track is used to rank each hypothesis and as new measurements are collected the process is repeated. In practice, using multi-hypothesis tracking to solve the multitarget, multisensor tracking problem often proves intractable due to the multitude of detections, the inaccuracy of the sensor measurements, and the possibility of spurious detections, such as caused by multipath or other deterministic and/or random affects.
In other approaches, such as Probabilistic Data Association (PDA) filtering and Joint Probabilistic Data Association (JPDA) filtering, sensor measurements are combined at each point in time according to an association probability to avoid excessive computation. The association probability may be determined in accordance with specific closed form analytical expressions, such as those used for the Kalman filter, as disclosed in "Tracking in a Cluttered Environment with Probabilistic Data Association," by Y.Bar-Shalom and E. Tse, published in Automatica, Vol. 11, pp. 451-460 (1973), and in "Sonar Tracking of Multiple Targets Using Joint Probabilistic Data Association," by T Fortmann, Y.Bar-Shalom, and M. Scheffe, published in IEEE Journal of Oceanic Engineering, Vol. OE8, No. 3, (July 1983).
These approaches suffer from several disadvantages. First, the calculation of association probabilities still requires excessive computations if the number of measurements per time interval is large. In addition, because the measurements typically have a nonlinear relationship with the target parameters, such as position or velocity, these so-called Kalman trackers may exhibit poor convergence behavior. Third, typical applications of these trackers, such as for use with sonic or electromagnetic target detection signals, suffer from poor environmental propagation conditions requiring the use of nonuniform probability of detection distributions in multitarget association; however, this technique also requires relatively simple analytical expressions for the probability distribution or density function. A need thus exists for an approach to the multitarget, multisensor tracking problem that overcomes the foregoing problems.